Vectorial AI
SAPIENS · Human Behavior Simulation
Patient & clinician populations

Every persona in the simulation traces back to real people

Vectorial has built a proprietary behavior model — SAPIENS. Each population is learnt from the behavior of thousands of real patients and clinicians — drawn from public and enterprise sources, and from self-reported care experience acquired privately through licensed research panels.

Live population sample Sampling the population ↓
Reddit condition communities Mayo Clinic Connect Licensed research panels Licensed panel interview Inspire HealthUnlocked
16,000+
Patients modeled across live healthcare deployments
Midi Health + Everkind
350+
Clinical patient journeys modeled — backstory, symptoms, scenarios
Midi Health
75+
Behavior traits modeled across every patient population
Midi Health
1,335
Simulations in a single month, replacing 1,000+ annotator hours
Everkind · 20% more holistic evaluation
01 · Grounded in real data

Every patient is modeled four dimensions deep — grounded in real user data.

Behavioral depth is the constraint in this category, not model architecture. SAPIENS learns each individual across the dimensions that actually move clinical behavior, and every one of them is derived from the behavior of real people.

Background & Demographics

Culture, community, belief systems, household composition and income tier.

e.g. tier-2 income, caregiver household, faith-informed care views
Behavior Traits

Socio-demographic and psychographic traits — risk tolerance, trust, adherence, care-seeking style.

e.g. peer-verifies before acting, cost-sensitive, low institutional trust
Environment & Scenarios

The settings and situations a patient moves through, and how their behavior changes across them.

e.g. post-dismissal, pre-diagnosis, researching alternatives at night
Experience & Exposure

The patient journey to date and prior exposure to treatments, clinicians and competing solutions.

e.g. two GPs seen, HRT declined once, currently self-treating
01.1 · Where the data comes from

We model population behavior from a wide variety of data sources — a highly accurate representation of how people actually behave, built while preserving anonymity throughout.

SUPPLY 01

Public behavioral signal at scale

A deep research agent finds the communities where a given patient or clinician population actually congregates, then ingests what they say unprompted — the closest thing to observing behavior without interrupting it.

Patient & clinician communities Reddit condition communities Mayo Clinic Connect HealthUnlocked Inspire Patient.info forums Drugs.com reviews WebMD drug reviews YouTube patient vlogs r/medicine r/nursing allnurses Student Doctor Network Medscape discussions
SUPPLY 02

Licensed panel interviews

We run our own structured interviews with real people in the condition area, through licensed panels, at a volume no research team can staff. These are ingested as first-party signal and used to enrich thin traits and correct the ones public sources got wrong.

First-party, consented Licensed panel interviews Recorded transcripts Consented respondents
SUPPLY 03

Licensed research panels

Panel integration gives access to verified, consented respondents with known demographics — including healthcare-specialist panels of screened patients, caregivers, nurses and physicians. Used to fill gaps where a population is under-represented online, most often older, rural and lower-income patients.

Verified panels Prolific Sermo · physicians M3 Global Research · HCPs Rare Patient Voice · patients & caregivers CloudResearch Connect Dynata Health Coverage correction
01.2 · Patient journey

Modeled across the care journey — outpatient, inpatient and discharge.

The same patient behaves differently in a clinic room, on a med-surg floor and on a discharge call. Every modeled patient carries a journey stage, and the simulation runs inside it.

Care setting drives behavior
OUTPATIENT
Clinic & pre-admission
Symptom history, self-research, consent and pre-op instructions — patients under-report and arrive with forum knowledge
INPATIENT
Admitted & post-op
Assessments, pain and mobility, flowsheet intake — patients hedge, guard, and can’t name what they feel
DISCHARGE
Transition home
Medication and activity instructions, home setup, what they didn’t ask before leaving
POST-DISCHARGE
Recovery & follow-up
Follow-up calls, adherence, when to escalate — and the questions they take back to communities instead

A knee-replacement patient describes pain very differently on post-op day two than on a week-two follow-up call. Pinning the simulation to the setting is what makes the transcript usable.

02 · Patient populations

Patient populations running inside health companies.

Live deployments at Midi Health and Everkind — actual audiences, with profile counts, signals and readiness as the platform reports them.

Midi Health
Customer
Virtual care clinic for women's health — perimenopause, menopause, postpartum, obesity, hair loss
6,000+
Patients modeled
75+
Behavior traits
350+
Clinical journeys
Perimenopause skincare users
Readiness91/100
Profiles 582Signals 68,407Trait groups 7
Perimenopause women
Readiness94/100
Profiles 600Signals 55,575Trait groups 7
Postpartum women
Readiness92/100
Profiles 564Signals 98,887Trait groups 7
General women's health
Readiness89/100
Profiles 532Signals 47,633Trait groups 7
Everkind
Customer
AI therapist platform — mental health patients, therapists and psychiatry
10,000+
Patients modeled
15+
Patient populations
1,000+
Annotator hrs replaced
Mental health patients
Readiness93/100
Profiles 2,749Populations 15+Trait groups 7
First responders
Readiness90/100
Profiles 733Signals 44,292Trait groups 7
Women in mid-life transition
Readiness91/100
Profiles 132Signals 35,978Trait groups 7
Relationship tension & breakdown
Readiness88/100
Profiles 210Signals 23,500Trait groups 7
03 · Clinician populations

Every clinician brings a protocol and a personality. We model both.

Treatment decisions aren't set by protocol alone. Every clinician brings their own philosophy and their own experience with patients — and how care feels is shaped as much by bedside behavior: how they build trust, how they question, how they extend empathy. We model that human side, not a robotic protocol. Populations are built from the communities where clinicians actually talk to each other, and every behavioral dimension is confidence-scored.

Live clinician sample · 94 modeled clinicians across 11 roles Sampling the population ↓
r/medicine & specialty subs Medscape discussions Student Doctor Network Licensed panel interview Nurse & NP communities Prolific · verified clinicians
How we model a clinician

Every clinician is modeled across dimensions extending beyond their specialty

Training & Practice Context

Specialty, care setting, years in practice, patient volume and case mix.

e.g. fellowship-trained, private ortho group, 700+ joints a year
Treatment Philosophy & Decision Style

Evidence thresholds, risk tolerance, guideline adherence, and when judgment overrides protocol.

e.g. operate late not early; guideline-concordant or justified in writing
Trust & Information Sources

Who they believe and in what order — peer networks, journals, communities, institutions, industry reps.

e.g. peer-verified first, institutional guidance checked against experience
Workflow & Constraints

Time pressure, documentation load, team structure, and how new tools actually get adopted or rejected.

e.g. 15-minute slots, inbox-driven day, anything adding clicks is dead
Modeled clinician audiences
Built from the communities where each role actually talks
1,900+
Clinicians modeled
11
Roles & specialties
Psychiatrists
READY FOR SIMULATION
Readiness92/100
Profiles 445Signals 28,235Trait groups 7
Therapists
READY FOR SIMULATION
Readiness90/100
Profiles 450Signals 16,429Trait groups 7
Nurses
READY FOR SIMULATION
Readiness91/100
Profiles 612Signals 33,148Trait groups 7
Dentists
READY FOR SIMULATION
Readiness89/100
Profiles 386Signals 19,764Trait groups 7
04 · Suki AI · Proof of concept

Patient & clinician populations already built for Suki AI.

Nurse and patient populations built for the settings Suki is deployed in — inpatient rooms and post-surgical wards. Below are the actual audiences, followed by full chat simulations generated from them.

Suki AI
Proof of concept
Ambient clinical intelligence for clinicians — modeling both sides of the encounter: surgical patients and the nurses who care for them
3,000+
Patients modeled
1,100+
Clinicians modeled
60+
Recovery journeys
Patient populations
Knee surgery patients
READY FOR SIMULATION
Readiness92/100
Profiles 418Signals 36,912Trait groups 7
C-section recovery
READY FOR SIMULATION
Readiness90/100
Profiles 376Signals 29,480Trait groups 7
Hip surgery patients
READY FOR SIMULATION
Readiness91/100
Profiles 342Signals 27,205Trait groups 7
Clinician populations
Nurse practitioners (NP)
READY FOR SIMULATION
Readiness93/100
Profiles 504Signals 31,860Trait groups 7
Registered nurses (RN)
READY FOR SIMULATION
Readiness91/100
Profiles 628Signals 38,417Trait groups 7
Ambient AI for Suki AI

How simulations can be used to evaluate & train across a vast variety of scenarios.

Ambient models have to pull the clinical points out of a conversation between two people. Patients don't speak in clean medical terms — they meander and circle back. Clinicians each draw that information out differently. We model both sides, so you get a full encounter instead of a monologue, and human-like data fast instead of one real encounter at a time.

Evaluate

Run ambient against thousands of conversations where the patient rambles, hedges, contradicts themselves, or has a family member talk over them — and see where accuracy drops before a customer does.

e.g. where does the note miss a medication the patient mentioned only once?
Train

Generate labeled training data across specialties, care settings and patient archetypes, with demographic ratios set to match your production distribution rather than whatever the last quarter happened to contain.

e.g. 5,000 orthopedic post-op transcripts, 80/20 gender split
Cover the long tail

Behavioral coverage comes from modeling real people, so the edge cases are the ones that actually occur — low health literacy, language hedging, distrust, conflated conditions, disfluencies and realistic transcription error.

e.g. the patient who says “my sugar thing” and means metformin
Suki products

Our modeled patient and physician population can be used across three products.

Suki’s products all sit inside the same encounter. Because both sides are modeled, that encounter can be simulated end to end — so quality gaps surface before production, not after.

PRODUCT 01

Ambient Documentation

Where we help: the conversation the note is built from — encounter dialogue at volume, fast and cheap, with the hedging real conversations carry.

How the modeled population helps
Patient audiences
  • Coverage across every stage of the patient journey
  • Patients from different backgrounds, each telling it their own way
  • Real-world noise: hedging, meandering, lay language
Clinician audiences
  • Every nurse and physician questions differently — all modeled
  • Both sides simulated, so you get a full encounter
Outcome
  • Evaluate note accuracy across the whole journey
  • Highly diverse training data, on demand
  • Solves the cold start when you add workflows or clinicians
PRODUCT 02

Assisted Revenue Cycle

Where we help: the clinical substance the code comes from — comorbidity profiles and treatment decisions spanning ICD-10, HCC, CPT and E/M, including the codes that are hard to reach.

How the modeled population helps
Patient audiences
  • Wide range of chronic conditions and comorbidities
  • The risk-adjusted long tail HCC capture depends on
  • The vagueness that creates specificity gaps
Clinician audiences
  • Varied assessments, orders and treatment plans
  • Coverage across ICD-10 and HCC codes
  • Realistic CPT and E/M levels, not uniformly clean visits
Outcome
  • Test charge capture and coding accuracy at scale
  • Catch the hard-to-reach codes before the denial does
PRODUCT 03

Clinical Reasoning

Where we help: the case volume an insight has to be right across — partial histories, lay descriptions, and clinicians with their own evidence thresholds.

How the modeled population helps
Patient audiences
  • Partial histories and symptoms described in lay language
  • Relevant facts volunteered late, the way they really are
  • Incomplete pictures, not tidy vignettes
Clinician audiences
  • Own evidence thresholds, risk tolerance and trusted sources
  • Real time pressure — a 15-minute slot, not an idealized one
  • Tests whether an insight lands with a skeptic
Outcome
  • See which insights hold up across a large volume of cases
  • Avoid insights drawn from a partial picture of the patient
  • A diverse dataset for clinicians to evaluate reasoning against
Sample chat simulations

What a simulated conversation actually reads like.

Transcripts generated from specific modeled profiles. Each profile’s background and journey stage set the scenario: the patient hedges and under-describes, the nurse probes and charts. Open any card for the full 24-turn transcript.

Simulation transcript · v1 · Session 10 · Chronic Condition Intake
Scheduled C-section · breech presentation
Modeled profile
PT-4102 C-section audience
Inpatient · post-op day 0–1
Life story
31, first baby, scheduled C-section for breech after a pregnancy she read her way through. Lives with her partner, no family in the city. Learned what to expect from other mothers online rather than from her discharge sheet, and arrives at every interaction already braced for the thing nobody warned her about.
Behavior trait
Under-describes pain until asked twice; redirects clinical questions toward the baby. Second-guesses every sensation and needs findings said plainly back to her.
Scenario
Scheduled C-section for breech presentation. Discomfort moving between lying and sitting, struggling to breastfeed through incision pain, but determined to establish a feeding routine.
Derived from Reddit condition communities · 1,204 signals
PROFILE PT-4102Completed 14/07/2026
Simulation transcript · v1 · Session 8 · Chronic Condition Intake
Scheduled C-section · chronic hypertension
Modeled profile
PT-4417 C-section audience
Inpatient · immediate post-op
Life story
36, chronic hypertension managed through the whole pregnancy on Labetalol. Spent nine months fighting to keep her numbers in range, so the scheduled section was itself an attempt to stay ahead of things. Tracks her own readings and notices when nobody volunteers them.
Behavior trait
Hypervigilant about a single clinical number and will interrupt an assessment to get it. Anxiety presents somatically — chest tightness — and she will not accept a made-up answer.
Scenario
Scheduled C-section with a history of chronic hypertension. Blood pressure monitored closely; concerned about medication adjustments and weaning off Labetalol.
Derived from Licensed research panel · 963 signals
PROFILE PT-4417Completed 14/07/2026
Simulation transcript · v1 · Session 1 · Chronic Condition Intake
Total hip replacement · PCA pump
Modeled profile
PT-2860 Hip Replacement audience
Inpatient · post-op day 0, PCA
Life story
58, total hip replacement after years of deterioration. Researched the surgery exhaustively in a hip-replacement group before admission and still checks his assumptions against the nurse. Has things at home that need handling and is already counting the days to losing the walker.
Behavior trait
Rations his own pain medication out of dependency fear. Cross-references every clinical answer against forum knowledge, and apologises for asking.
Scenario
Immediate recovery after total hip replacement, managing pain with a PCA pump. Pragmatic outlook — wants effective relief while minimizing opioids over dependency concerns.
Derived from HealthUnlocked · 1,417 signals
PROFILE PT-2860Completed 14/07/2026
05 · Why it’s different

A grounded population, not a language model playing a patient.

Ask a general-purpose model to play a 47-year-old with perimenopause and it gives you the reasonable answer — that's what it was trained to do. Real patients delay, distrust, self-treat and decide on cost and stigma. That behavior only survives if the population carries it in from real people.

 
Prompted LLM persona
SAPIENS grounded profile
Origin
Written from a prompt or a segment definition
Learnt from the observed behavior of real patients in that condition area
Diversity
Reproduces the assumptions in the prompt and regresses to the median patient
Distribution inherited from the real population — culture, income tier, care access, prior treatment history
Backstory
Invented to sound plausible
Grounded in real journeys: when symptoms started, who was seen, what was tried, what failed
Context
Answers in a vacuum, the same way every time
Answers from inside the journey stage that patient is actually in
Under pressure
Agrees, normalizes, and gives the rational answer
Holds the avoidance, distrust and cost-driven behavior that real patients show
Auditability
No provenance — you cannot ask where an answer came from
Every trait traces back to signals and interview transcripts, each confidence-scored
05.1 · Measured accuracy · SAPIENS vs LLMs

SAPIENS vs general purpose LLMs on human behavior simulations benchmarking

The differences above are measurable. Accuracy is the % of opinions where the real opinion matches the generated one when a product is shown to a modeled user.

frontier ceiling
53.2%
47.6%
49.95%
51.2%
50.4%
49.85%
86.1%
o3Apr 2025
GPT-5Aug 2025
GPT-5.2Dec 2025
Sonnet 4.6Feb 2026
GPT-5.5Apr 2026
Opus 4.8May 2026
SAPIENS 

Benchmark developed with Berkeley AI Research (BAIR). Full methodology, per-model and per-domain results: SAPIENS Benchmarking Study.

06 · Testimonials

Judged by experts

When Vectorial showed the perimenopause audience simulations, it immediately made sense to us. The model captured behavioral traits and motivations consistent with what we see from real patients, which made the audience feel credible and specific.
Laura Moon
VP Product · Midi Health
Vectorial's value is that the personas are learnt from real people, not synthetic data — something no evaluation engine has been able to close the gap on for us in a meaningful way.
Supreet Pal Singh
CTO · Everkind
The chat simulations feel natural, representing real-life conversations between patients and nurses — and they are clinically accurate.
Nandita Kamath
Director of Nursing Solutions · Suki AI